This application addresses RFA RM-09-006: Novel statistical methods for human gene expression quantitative trait loci (eQTL) analysis. Genome-wide association studies (GWAS) are rapidly becoming the preferred approach for discovery of phenotype- genotype associations; however, statistical power, replication and validation of candidates remain to be challenging. In addition to genotypes, gene expression data are now being collected along with disease and exposure data in large human cohorts, across multiple tissues, and in animal experiments. We will test the hypothesis that expression quantitative trait locus (eQTL) analysis is an effective and mechanistically- relevant approach to the discovery and validation of candidate genomic loci/genes that control biological pathways and networks, using expression data from various tissues, from disease vs. normal conditions, or under experimental perturbation. The ultimate goal is to elucidate the underpinnings of human disease. In this project we will develop new statistical tools and graphical user interface-enabled software to handle these diverse data streams. The primary goal of the analysis is to identify the interactions among genetic polymorphisms, expression, and tissue type or phenotype, which would not be found using traditional GWAS. We have assembled an experienced team of biomedical scientists, statistical geneticists, and statisticians, and we already laid out the methodological and computational groundwork for the statistical approaches. In addition, we have a track record of successful software development, and we have already begun building user-friendly eQTL software aimed at the broad scientific community. We describe how a number of key remaining challenges in applying eQTL mapping to large-scale GWAS studies will be addressed in a two-year period by: (i) enabling fast and statistically rigorous eQTL analyses in large homo- and hetero-zygous populations; (ii) developing fast ANOVA-based modeling of expression as a function of genotype and tissue type; (iii) modeling phenotypic traits as a function of expression and genotype; and (iv) indentifying patterns of significant individual-transcript associations using biclustering.